{"id":2830158,"url":"https://alion.io/job/qualcomm-embedded-ml-staff-engineer-snapdragon-mobile","title":"Embedded ML Staff Engineer -- Snapdragon Mobile","company":{"id":94,"name":"Qualcomm","domain":"qualcomm.com","url":"https://alion.io/company/qualcomm","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Eightfold","truth_index":{"grade":"B","score":71,"open_postings":1087,"ghost_share":0.231,"stale_share":0.14,"repost_share":0.453,"time_to_fill_p50_days":74,"computed_at":"2026-10-24T06:02:35Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"staff","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["San Diego, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":223600,"max":335400,"currency":"USD","period":"year","gross":null,"usd_annual":335400},"salary_estimate":null,"experience_years_min":8,"visa_sponsorship":false,"relocation_package":false,"has_equity":true,"technologies":[{"name":"AI Agents","optional":false},{"name":"Computer Vision","optional":false},{"name":"Machine Learning","optional":false},{"name":"Agile","optional":true},{"name":"C++","optional":true},{"name":"Fine-tuning","optional":true},{"name":"Function Calling","optional":true},{"name":"Java","optional":true},{"name":"Kotlin","optional":true},{"name":"KV Cache","optional":true},{"name":"LLM","optional":true},{"name":"LoRA","optional":true},{"name":"Model Context Protocol","optional":true},{"name":"Multimodal AI","optional":true},{"name":"PEFT","optional":true},{"name":"Python","optional":true},{"name":"PyTorch","optional":true},{"name":"Rest-Assured","optional":true},{"name":"Sensor Fusion","optional":true},{"name":"State Management","optional":true},{"name":"TensorFlow","optional":true},{"name":"TensorFlow C++","optional":true},{"name":"TypeScript","optional":true}],"status":"live","first_seen_at":"2026-10-12T00:00:00Z","employer_posted_date":"2026-10-18","last_verified_at":"2026-10-24T07:25:39Z","board_verified":true,"closed_at":null,"days_open":12,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":12},"description":"Company:\nQualcomm Technologies, Inc.Job Area:\nEngineering Group, Engineering Group > Machine Learning EngineeringGeneral Summary:\nSeeking an experienced Embedded ML Staff Engineer focused on Snapdragon Mobile products. As a senior individual contributor on the team working on Mobile Handset software architecture and system design and development, you will analyze requirements and design/implement solutions for new use-cases and features. You will drive technical initiatives to evolve Snapdragon Mobile AI software, helping deliver performance and power enhancements into the SW/HW stacks and tools of state-of-the-art machine learning solutions. You will work closely with architects, development leads, software and hardware engineers, project engineers, and product managers.\nIdeal candidate has strong experience with Mobile SW design and deployment, system SW performance optimization, benchmarking, and performance breakdown analysis with CPU, GPU, NPU, and knowledge of state of the art in AI for one or more of the domains such as Computer Vision, Audio, Generative AI. Hands-on experience with agentic AI systems, LLMs/SLMs, and embedded ML at the system level on resource-constrained devices, including heterogeneous compute placement across CPU/GPU/NPU and context/perception pipelines that feed those experiences, is a strong plus. Experience with Mobile platforms is highly desirable.\nStrong written and verbal communication skills are important. Strong collaboration and mentorship skills are a plus.\nMinimum Qualifications:\n• Bachelor's degree in Computer Science, Engineering, Information Systems, or related field and 8+ years of Hardware Engineering, Software Engineering, Systems Engineering, or related work experience.OR\nMaster's degree in Computer Science, Engineering, Information Systems, or related field and 7+ years of Hardware Engineering, Software Engineering, Systems Engineering, or related work experience.\nOR\nPhD in Computer Science, Engineering, Information Systems, or related field and 6+ years of Hardware Engineering, Software Engineering, Systems Engineering, or related work experience.\nResponsibilities \nDesign and implement architecture for complex AI features and experiences within your subsystem for Snapdragon Mobile products; propose architecture improvements.\nResearch and collaborate cross-functionally to help define requirements; build hands-on prototypes.\nDesign and implement on-device agentic experience components, including context and memory management, perception (e.g., screen/scene understanding, sensor fusion), and heterogeneous compute placement for LLM/SLM workloads across CPU, GPU, and NPU under tight power, thermal, and memory budgets.\nImplement closed-loop, telemetry-driven resource scheduling components for heterogeneous compute (CPU/GPU/NPU), re-evaluating workload placement against live power, thermal, and SLA signals.\nImplement memory and model-state lifecycle management for on-device LLM/SLM serving -- including KV-cache and context-state caching, eviction, paging, and reuse/sharing across sessions under tight device memory budgets.\nEvaluate and help architect around memory bandwidth and data-movement constraints (e.g., high-bandwidth memory, on-chip interconnects) to maximize achievable AI throughput and efficiency on constrained silicon.\nServe as a technical resource in design reviews; recommend improvements aligned to business and customer needs.\nCollaborate with external partners on subsystem implementation plans and milestones.\nStay current on AI and Mobile industry trends, competitors' products, and advances in various engineering fields; share expertise within the team.\nContribute technical input and trend analysis to roadmap discussions led by architecture and product leadership.\nPreferred Qualifications \nExcellent understanding of AI frameworks (e.g., TensorFlow, PyTorch). Experience with large language models/foundational models and small language models (SLMs), including LoRA/adapter-based fine-tuning and on-device personalization techniques, MCP's a plus.\nExperience with agentic AI architectures -- planning, tool-use/orchestration, context and memory management -- and their deployment on resource-constrained embedded devices.\nExperience in architecting, designing, and implementing SW frameworks for complex heterogeneous or multi-processor systems, including runtime placement of ML/agentic workloads across CPU, GPU, and NPU.\nExperience designing graph-based (DAG) or state-machine-driven schedulers for multi-workload resource allocation across heterogeneous compute, with closed-loop adaptation to live system telemetry.\nExperience with resource allocation across multiple concurrent AI workloads/agents competing for shared compute and memory, not just single-model placement.\nBackground in closed-loop or control-systems thinking (feedback-driven scheduling, adaptive/self-correcting systems) is a plus.\nExperience with embedded ML at the system level -- model deployment, scheduling, and lifecycle management under tight memory, power, and thermal constraints on mobile/embedded platforms.\nExperience with LLM/SLM serving internals -- KV-cache and model-state management, paging/eviction strategies, and state sharing or reuse across concurrent sessions or agents.\nGood Understanding of complete AI Software stack and familiarity with AI hardware acceleration technologies.\nUnderstanding of memory subsystem and interconnect bandwidth (e.g., HBM/LPDDR, cache hierarchies, NPU memory architecture) and how bandwidth -- not just compute -- bottlenecks AI inference performance; experience architecting to minimize redundant data movement.\nExperience in modeling AI networks and workloads to extract performance and power estimates, and converting that into optimization.\nExperience with context and perception pipelines (sensor fusion, screen/scene understanding, multimodal context) that power personalized or agentic on-device experiences.\nStrong background in algorithm development, performance analysis using profiling tools, and algorithmic modification methods for performance improvement.\nKnowledge of state of the art in AI for one or more domains such as Computer Vision, Audio, Generative AI.\nKnowledge of Mobile platforms, Android framework, Embedded system implementations.\nStrong software engineering principles.\nProficiency in programming languages such as C, C++, Python. Understanding Typescript, Java, Kotlin is a plus. The ability to use agentic tooling for development is a plus. \nExcellent communication skills to articulate complex technical concepts to non-technical and technical stakeholders.\nExperience mentoring junior and mid-level engineers; ability to influence technical direction without formal authority.\nDetail-oriented with strong problem-solving, analytical, and debugging skills with the ability to think strategically and drive innovative solutions.\nDemonstrated ability to learn, think and adapt in a fast-changing environment.\nFamiliarity with software development methodologies, version control systems, and agile project management practices.\n5-8 years of experience in High-Performance Computing System Engineering or Software, with 2+ years in AI system optimization.\nBachelor's or Master's in Computer Science or Electrical Engineering.\nQualcomm is an equal opportunity employer. If you are an individual with a disability and need an accommodation during the application/hiring process, rest assured that Qualcomm is committed to providing an accessible process. You may e-mail  or call Qualcomm's toll-free number found here. Upon request, Qualcomm will provide reasonable accommodations to support individuals with disabilities to be able participate in the hiring process. Qualcomm is also committed to making our workplace accessible for individuals with disabilities. (Keep in mind that this email address is used to provide reasonable accommodations for individuals with disabilities. We will not respond here to requests for updates on applications or resume inquiries).\nTo all Staffing and Recruiting Agencies:Our Careers Site is only for individuals seeking a job at Qualcomm. Staffing and recruiting agencies and individuals being represented by an agency are not authorized to use this site or to submit profiles, applications or resumes, and any such submissions will be considered unsolicited. Qualcomm does not accept unsolicited resumes or applications from agencies. Please do not forward resumes to our jobs alias, Qualcomm employees or any other company location. Qualcomm is not responsible for any fees related to unsolicited resumes/applications.\nEEO Employer: Qualcomm is an equal opportunity employer; all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, Veteran status, or any other protected classification.\nQualcomm expects its employees to abide by all applicable policies and procedures, including but not limited to security and other requirements regarding protection of Company confidential information and other confidential and/or proprietary information, to the extent those requirements are permissible under applicable law.\nPay rangeand Other Compensation & Benefits :\n$223,600.00 - $335,400.00The above pay scale reflects the broad, minimum to maximum, pay scale for this job code for the location for which it has been posted. Even more importantly, please note that salary is only one component of total compensation at Qualcomm. We also offer a competitive annual discretionary bonus program and opportunity for annual RSU grants (employees on sales-incentive plans are not eligible for our annual bonus). In addition, our highly competitive benefits package is designed to support your success at work, at home, and at play. Your recruiter will be happy to discuss all that Qualcomm has to offer - and you can review more details about our US benefits at this link.\nIf you would like more information about this role, please contact Qualcomm Careers.","description_format":"text","description_chars":10124,"description_truncated":false,"requirements":{"experience_years_min":8,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"bachelor","optional":false},"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Processors, MCUs & AI Chips","Autonomous Driving","RF & Wireless Hardware","Car Electronics"],"lifecycle":[{"event":"open","at":"2026-10-19T02:38:25Z"}],"visa":[{"country":"US","licensed_sponsor":true,"evidence":"H-1B filings in 12 months: 55 · green card filings: 6","filings_12m":55,"filings_prev_12m":87,"green_card_filings_12m":6,"median_offered_wage_usd":122008,"route":null,"cap_exempt":false,"checked_at":"2026-10-15T11:24:14+00:00","sources":["US Department of Labor: LCA disclosure data (H-1B, H-1B1, E-3)","US Department of Labor: PERM disclosure data (green cards)"],"filings_for_role_12m":25}],"liveness":{"score":89,"band":"hot","label":"Hiring now","p_open":1,"p_active":0.885,"p_room":1,"age_days":12,"expected_fill_days":74,"reasons":["conf:2","velocity","win:early","comp:brand"],"computed_at":"2026-10-24T06:02:35Z"},"pay":{"stated_usd_annual":335400,"is_top_pay":true},"html_url":"https://alion.io/job/qualcomm-embedded-ml-staff-engineer-snapdragon-mobile","json_url":"https://alion.io/job/qualcomm-embedded-ml-staff-engineer-snapdragon-mobile.json","meta":{"generated_at":"2026-10-24T11:19:22Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","about":"Alion is a live layer of people, companies and AI agents: who they are, whether they are real and active right now, what they do and how to work with them, readable by people and by agents and paid per call.","catalog":"https://alion.io/catalog.json","usage":{"tier":"crawler_verified","counted_by":"address","units_charged":1,"used_today":16057,"day_limit":null,"remaining_today":null,"minute_limit":300,"resets_at":"2026-10-25T00:00:00Z"}},"offers":[{"id":"company.slices","title":"One company in depth, by slice","status":"live","price":{"credits":0.02,"usd":0.002,"plus_per_slice":{"credits":0.05,"usd":0.005}},"unit":"per company, plus each slice with data","note":"the employer in depth","call":{"mcp_tool":"get_company","arguments":{"id":94},"rest":"https://alion.io/mcp/rest/get_company?id=94"},"human":"https://alion.io/catalog?offer=company.slices&for=job%2Fqualcomm-embedded-ml-staff-engineer-snapdragon-mobile"},{"id":"market.stats","title":"A market slice: pay, demand and time to fill","status":"live","price":{"credits":1,"usd":0.1},"unit":"per slice","note":"pay, demand and time to fill for this role and place","call":{"mcp_tool":"market_stats"},"human":"https://alion.io/catalog?offer=market.stats&for=job%2Fqualcomm-embedded-ml-staff-engineer-snapdragon-mobile"},{"id":"job.search","title":"Open jobs by role, technology, place, pay and visa","status":"live","price":{"credits":0.02,"usd":0.002},"unit":"per posting in a list","note":"similar open postings","call":{"mcp_tool":"search_jobs"},"human":"https://alion.io/catalog?offer=job.search&for=job%2Fqualcomm-embedded-ml-staff-engineer-snapdragon-mobile"},{"id":"company.verify","title":"Is this company real and active right now","status":"pilot","price":null,"unit":"per company","request":{"url":"https://alion.io/catalog/request","method":"POST","body":"{\"offer\": \"company.verify\", \"for\": \"job/qualcomm-embedded-ml-staff-engineer-snapdragon-mobile\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Fqualcomm-embedded-ml-staff-engineer-snapdragon-mobile"}]}